The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian; when it is applicable, it is much more efficient than the particle filter.
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The ensembleKalmanfilter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential...
In estimation theory, the extended Kalmanfilter (EKF) is the nonlinear version of the Kalmanfilter which linearizes about an estimate of the current...
For statistics and control theory, Kalmanfiltering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements...
Karlheinz Stockhausen Musical ensemble Distribution ensemble or probability ensemble (cryptography) EnsembleKalmanfilterEnsemble learning (statistics and...
gradient method or the generalized minimal residual method. The ensembleKalmanfilter is sequential method that uses a Monte Carlo approach to estimate...
forecast EnsembleKalmanfilterEnsemble (fluid mechanics) Forecasting Probabilistic forecasting THORPEX Interactive Grand Global Ensemble North American...
Alpha beta filter Data assimilation EnsembleKalmanfilter Extended Kalmanfilter Invariant extended Kalmanfilter Fast KalmanfilterFiltering problem (stochastic...
models poses special challenges, and standard methods, such as the ensembleKalmanfilter (EnKF) do not work well. Statistical variability of corrections...
systems with significant energy barriers Hybrid Monte Carlo EnsembleKalmanfilter — recursive filter suitable for problems with a large number of variables...
the Laplace assumption. Unlike classical (e.g. Kalman-Bucy or particle) filtering, generalized filtering eschews Markovian assumptions about random fluctuations...
1103/physrevlett.53.2187. ISSN 0031-9007. Ott, E.; et al. (2004). "A Local EnsembleKalmanFilter for Atmospheric Data Assimilation". Tellus A. 56 (5): 415–428. doi:10...
parameter estimation of hydrologic models using the constrained ensembleKalmanfilter". Water Resources Research. doi:10.1029/2008WR007401 "Sequential...
of any attempt to use KalmanFilter for multi-decadal ocean reanalyses." The 4-Dimensional Local Ensemble Transform KalmanFilter (4D-LETKF) has been applied...
The North American Ensemble Forecast System (NAEFS) is a joint project involving the Meteorological Service of Canada (MSC) in Canada, the National Weather...
Standardized Qualification of Whole-House Energy Savings Predictions by Calibration to Energy Use History https://kalman-filter.com/root-mean-square-error...
as "platform", "engine", or "algorithm"), is a subclass of information filtering system that provides suggestions for items that are most pertinent to...
Lauze, François; Pedersen, Kim Steenstrup (2013-05-01). "Unscented KalmanFiltering on Riemannian Manifolds". Journal of Mathematical Imaging and Vision...
"Mutually Interactive State/Parameter Estimation (MISP)". Application of KalmanFilter to Hydrology, Hydraulics and Water Resources. Univ. of Pittsburgh, Pennsylvania...
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